Giunchi, Daniele;
Sztrajman, Alejandro;
Steed, Anthony;
(2022)
Fast Blue-Noise Generation via Unsupervised Learning.
In:
Proceedings of the 2022 IEEE Congress on Computational Intelligence (WCCI 2022).
IEEE: Padova, Italy.
(In press).
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Abstract
—Blue noise is known for its uniformity in the spatial domain, avoiding the appearance of structures such as voids and clusters. Because of this characteristic, it has been adopted in a wide range of visual computing applications, such as image dithering, rendering and visualisation. This has motivated the development of a variety of generative methods for blue noise, with different trade-offs in terms of accuracy and computational performance. We propose a novel unsupervised learning approach that leverages a neural network architecture to generate blue noise masks with high accuracy and real-time performance, starting from a white noise input. We train our model by combining three unsupervised losses that work by conditioning the Fourier spectrum and intensity histogram of noise masks predicted by the network. We evaluate our method by leveraging the generated noise for two applications: grayscale blue noise masks for image dithering, and blue noise samples for Monte Carlo integration.
Type: | Proceedings paper |
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Title: | Fast Blue-Noise Generation via Unsupervised Learning |
Event: | 2022 IEEE Congress on Computational Intelligence (WCCI 2022) |
Location: | Padova, Italy |
Dates: | 18 Jul 2022 - 23 Jul 2022 |
Open access status: | An open access version is available from UCL Discovery |
Publisher version: | https://wcci2022.org/ |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Neural networks, Image processing, Noise generators, Monte Carlo methods |
UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10149128 |
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